Sepsis, or illness caused by a severe infection, is the third leading cause of infectious death (Bone, et al., 1997). The majority of sepsis cases are caused by bacterial infections. Accordingly, a substantial amount of money and time has gone into the search for drugs effective to treat sepsis and/or eliminate the pathogens (e.g., bacteria) which cause sepsis.
Current animal models of sepsis, used primarily to evaluate the efficacy of compounds in treating sepsis, are typically “death as an endpoint” models. In other words, the determination of whether an animal has succumbed to sepsis (or if a particular treatment was effective) is made by scoring whether the animal dies during the course of the experiment.
Such death-as-an-endpoint models have a number of disadvantages. For example, because the investigator must wait until animals die before useful data may be obtained from the study, the study takes longer than it would if such data could be obtained at a time before death of the animals. From an animal welfare standpoint, death-as-an-endpoint models are disadvantageous because the animals die directly from the disease or toxic compounds under study, and are thus more likely to suffer as compared with animals that are euthanized by humane means earlier in the study.
It would therefore be desirable to have a reliable method by which an investigator could accurately predict whether and when a particular animal is likely to die as a result of sepsis without actually waiting for the animal to do so. The present invention provides such a method, as well as related methods suitable for screening drugs effective to treat sepsis